代码搜索：evaluate
找到约 3,619 项符合「evaluate」的源代码
代码结果 3,619
www.eeworm.com/read/143003/7144819
cc eval.cc
// eval.cc
//--------------------------------------------------------------------------
// This code is a component of Genetic Programming in C++ (Version 0.40)
// Copyright Adam P. Fraser, 1993,
www.eeworm.com/read/459616/7270284
cpp poly.cpp
// evaluate a polynomial
#include
template
T PolyEval(T coeff[], int n, const T& x)
{// Evaluate the degree n polynomial with
// coefficients coeff[0:n] at the point x.
T y
www.eeworm.com/read/459616/7270669
cpp horner.cpp
// evaluate a polynomial using Horner's rule
#include
template
T Horner(T coeff[], int n, const T& x)
{// Evaluate the degree n polynomial with
// coefficients coeff[0:n] at t
www.eeworm.com/read/458538/7294781
m gqa.m
%gqa
n=input('please input population size n=:');%群体规模
g=input('please input max-generation g=:');%进化代数
for number=1:30
clc
t=0;
initialize;%初始化
observe;%观测染色体，将量子态转化为二进制的问题
www.eeworm.com/read/458493/7295786
m linterp.m
function yi = linterp(x,y,xi)
% linterp Piecewise linear interpolation in a table of (x,y) data
%
% Synposis: yi = linterp(x,y,xi)
%
% Input: x,y = vectors containing the tabulated data
www.eeworm.com/read/458488/7296162
m linterp.m
function yi = linterp(x,y,xi)
% linterp Piecewise linear interpolation in a table of (x,y) data
%
% Synposis: yi = linterp(x,y,xi)
%
% Input: x,y = vectors containing the tabulated data
www.eeworm.com/read/450951/7474279
cpp main.cpp
#include "Common.h"
#include "TagInference.h"
using namespace std;
void main()
{
CTagInference pTI;
pTI.Evaluate();
}
www.eeworm.com/read/448905/7522772
m fig2_41.m
% Chapter 2: Figure 2.41, p. 83
% Using conv and polyval to multiply and evaluate
% the polynomials (3 s^2 + 2 s + 1 ) ( s + 4).
%
p=[3 2 1]; q=[1 4];
n=conv(p,q)
%
value=polyval(n,-5)
www.eeworm.com/read/299227/7875041
cpp evaluatem.cpp
//计算后缀表达式的值Evaluatem.cpp
#include
#include
#include
#include
typedef float ElemType;
#include "Evaluate.cpp"
void main()
{char p[40];
float y;
p
www.eeworm.com/read/198970/7899959
m ise.m
function v = ise(p,q,type)
%
% ise(p,q [,'type']) -- estimate the integrated squared error between
% two densities p,q
% type:
% [double] -- use "epsilon-exact" prod